source("utils.R")POC modeled VS POC observed
Investigate whether modeled POC is representative of POC observations.
POC observation data / sediment traps
Read data
Read data, keep columns of interest, rename, drop observations where poc is missing, and keep only sediment traps.
df_trap_raw <- read_delim("data/raw/GO_flux.tab", delim = "\t", escape_double = FALSE, trim_ws = TRUE, skip = 87, show_col_types = FALSE)
# Select columns of interest and rename
df_trap <- df_trap_raw %>%
select(
id_ref = `ID (Reference identifier)`,
id_loc = `ID (Unique location identifier)`,
type = `Type (Data type)`,
lon = Longitude,
lat = Latitude,
depth = `Depth water [m] (Sediment trap deployment depth)`,
start_date = `Date/Time (Deployed)`,
end_date = `Date/time end (Retrieved)`,
duration = `Duration [days]`,
poc = `POC flux [mg/m**2/day]`
) %>%
# drop observations where poc is missing
drop_na(poc) %>%
# only sediment traps
filter(type == "sediment_trap")Distribution in space and time
Number of observations at each location.
df_trap %>%
add_count(lon, lat) %>%
ggplot() +
geom_polygon(data = world, aes(x = lon, y = lat, group = group), fill = "grey") +
geom_point(aes(x = lon, y = lat, colour = n)) +
#scale_colour_cmocean(name = "deep") +
scale_colour_viridis_c(trans = "log10") +
coord_quickmap(expand = 0)Depth distribution
ggplot(df_trap) + geom_histogram(aes(x = depth), binwidth = 100)Time distribution
ggplot(df_trap) + geom_histogram(aes(x = start_date))ggplot(df_trap) + geom_histogram(aes(x = duration)) + scale_x_log10()Data points around the 1000 m horizon
Let’s focus on the data around 1000 m depth. For a set of depth range between 50 and 500 m, compute number and map observations.
hor <- 1000
ranges <- lapply(seq(from = 100, to = 500, by = 50), function(W){
d <- df_trap %>%
filter(between(depth, hor - W, hor + W)) %>% # keep points within the depth range
mutate(W = W)
return(d)
})
ranges <- do.call(bind_rows, ranges)
ranges %>%
count(W) %>%
ggplot() +
geom_col(aes(x = W, y = n)) +
ggtitle("Number of data points within 1000 ± W depth range")ranges %>%
ggplot() +
geom_polygon(data = world, aes(x = lon, y = lat, group = group), fill = "grey") +
geom_point(aes(x = lon, y = lat, colour = depth)) +
scale_colour_cmocean(name = "deep") +
coord_quickmap(expand = 0) +
facet_wrap(~W)Fixed depth range
Let’s use a depth range of 200 meters. Look at the distribution of POC values (transformed and logged).
W <- 500
df_trap <- df_trap %>%
filter(between(depth, hor - W, hor + W)) %>%
mutate(poc_log = log(poc))
ggplot(df_trap) + geom_histogram(aes(x = poc))ggplot(df_trap) + geom_histogram(aes(x = poc_log)) # yayLog-transformed poc is closer to a normal distribution, but if we want to predict modeled poc from observed poc, depending on the prediction model, the distribution of the predictor may not be important.
Let’s now round coordinates to match with Wang et al., 2023 which uses a grid of 2°×2°.
# average by pixel
df_trap_pix <- df_trap %>%
mutate(
# floor longitude and add 1 because carbon longitudes are odd
lon = roundp(lon, precision = 2, f = floor) + 1,
# round latitude because carbon latitudes are even
lat = roundp(lat, precision = 2, f = round)
) %>%
group_by(lon, lat) %>%
summarise(
poc_mean = mean(poc),
poc_sd = sd(poc)
) %>%
ungroup()
df_trap_pix %>%
ggplot() +
geom_polygon(data = world, aes(x = lon, y = lat, group = group), fill = "grey") +
geom_point(aes(x = lon, y = lat, colour = poc_mean)) +
scale_colour_cmocean(name = "matter") +
coord_quickmap(expand = 0)df_trap_pix %>%
ggplot() +
geom_polygon(data = world, aes(x = lon, y = lat, group = group), fill = "grey") +
geom_point(aes(x = lon, y = lat, colour = poc_sd)) +
scale_colour_viridis_c() +
coord_quickmap(expand = 0)Let’s check the number of observations per pixel.
df_trap %>%
mutate(
# floor longitude and add 1 because carbon longitudes are odd
lon = roundp(lon, precision = 2, f = floor) + 1,
# round latitude because carbon latitudes are even
lat = roundp(lat, precision = 2, f = round)
) %>%
count(lon, lat) %>%
ggplot() +
geom_polygon(data = world, aes(x = lon, y = lat, group = group), fill = "grey") +
geom_point(aes(x = lon, y = lat, colour = n)) +
scale_colour_viridis_c(trans = "log10") +
coord_quickmap(expand = 0)Looks like we have 2 timeseries with > 300 observations.
Match with POC modeled data
Read modeled data
d_mat <- readMat("data/raw/Cexp_CAFE_kl24h.mat")$EXP[,,1]
poc_exp <- d_mat$POCexp # POC at base of euphotic layer (73 m)
poc_100 <- d_mat$POC100 # POC at 100 m
poc_1000 <- d_mat$POC1000 # POC at 1000 m
# Generate colnames as longitudes and rownames as latitudes
colnames(poc_exp) <- (c(0.5:179.5) * 2)
rownames(poc_exp) <- (c(0:90) * 2) - 90
colnames(poc_100) <- (c(0.5:179.5) * 2)
rownames(poc_100) <- (c(0:90) * 2) - 90
colnames(poc_1000) <- (c(0.5:179.5) * 2)
rownames(poc_1000) <- (c(0:90) * 2) - 90
## Convert to dataframe
df_exp <- poc_exp %>%
as.data.frame() %>%
rownames_to_column(var = "lat") %>%
as_tibble() %>%
pivot_longer(cols = -lat, names_to = "lon", values_to = "poc_exp") %>%
mutate(lat = as.numeric(lat), lon = as.numeric(lon)) %>%
mutate(lon = ifelse(lon > 180, lon - 360, lon)) %>%
mutate(poc_exp = ifelse(poc_exp == 0, NA, poc_exp)) %>%
arrange(lon, lat)
df_100 <- poc_100 %>%
as.data.frame() %>%
rownames_to_column(var = "lat") %>%
as_tibble() %>%
pivot_longer(cols = -lat, names_to = "lon", values_to = "poc_100") %>%
mutate(lat = as.numeric(lat), lon = as.numeric(lon)) %>%
mutate(lon = ifelse(lon > 180, lon - 360, lon)) %>%
mutate(poc_100 = ifelse(poc_100 == 0, NA, poc_100)) %>%
arrange(lon, lat)
df_1000 <- poc_1000 %>%
as.data.frame() %>%
rownames_to_column(var = "lat") %>%
as_tibble() %>%
pivot_longer(cols = -lat, names_to = "lon", values_to = "poc_1000") %>%
mutate(lat = as.numeric(lat), lon = as.numeric(lon)) %>%
mutate(lon = ifelse(lon > 180, lon - 360, lon)) %>%
mutate(poc_1000 = ifelse(poc_1000 == 0, NA, poc_1000)) %>%
arrange(lon, lat)
# Join together
df_mod <- df_exp %>% left_join(df_100, by = join_by(lat, lon)) %>% left_join(df_1000, by = join_by(lat, lon))
# Convert from mmol C m-2 year-1 to mg C m-2 day-1 (divide by 365.25 and multiply by 12)
df_mod <- df_mod %>%
mutate(
poc_exp = (poc_exp / 365.25)*12,
poc_100 = (poc_100 / 365.25)*12,
poc_1000 = (poc_1000 / 365.25)*12
)
# Plot maps
ggmap(df_mod, var = "poc_exp", type = "raster") +
scale_fill_cmocean(name = "matter", na.value = NA) +
geom_point(data = df_trap_pix, aes(x = lon, y = lat), size = 0.5)ggmap(df_mod, var = "poc_100", type = "raster") +
scale_fill_cmocean(name = "matter", na.value = NA) +
geom_point(data = df_trap_pix, aes(x = lon, y = lat), size = 0.5)ggmap(df_mod, var = "poc_1000", type = "raster") +
scale_fill_cmocean(name = "matter", na.value = NA) +
geom_point(data = df_trap_pix, aes(x = lon, y = lat), size = 0.5)Join
Join by coordinates, drop observations where modeled poc is not available.
df_all <- df_trap_pix %>%
left_join(df_mod, by = join_by(lon, lat)) %>%
drop_na(poc_100, poc_1000)Let’s plot a map of our final dataset.
ggplot(df_all) +
geom_polygon(data = world, aes(x = lon, y = lat, group = group), fill = "grey") +
geom_point(aes(x = lon, y = lat)) +
coord_quickmap(expand = 0)Let’s also have a look at the data distribution.
ggplot(df_all) + geom_histogram(aes(x = poc_mean))ggplot(df_all) + geom_histogram(aes(x = poc_1000))Let’s still plot modeled VS observations, both in untransformed and log-transformed spaces.
ggplot(df_all) +
geom_point(aes(x = poc_mean, y = poc_1000)) +
geom_abline(intercept = 0, slope = 1, colour = "red")ggplot(df_all) +
geom_point(aes(x = poc_mean, y = poc_1000)) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
scale_x_log10() +
scale_y_log10()Let’s compute the correlation.
# Untransformed
cor(df_all$poc_mean, df_all$poc_1000)[1] 0.1169209
# Log-transformed
cor(log(df_all$poc_mean), log(df_all$poc_1000))[1] 0.2019889
POC observation data / ARGO
library(raveio)
# Open mat file
globe <- read_mat("data/raw/BGC_Argo_monthly_maps_GLOBESINK_global_beta20240212auto_baseline_QC_only_fluxes.mat")
# Read POC data
# NB: these are mg C m-2 day-1, same as Wang
poc_l <- globe$`monthly_maps/flux/POC_l`
poc_s <- globe$`monthly_maps/flux/POC_s`
# Read coordinates
# compute bin centres from edges
depths <- globe$zbin_edges[1,]
depths <- head(depths, -1) + (diff(depths) / 2)
lats <- globe$lat_edges[1,]
lats <- head(lats, -1) + (diff(lats))/2
lons <- globe$lon_edges[1,]
lons <- head(lons, -1) + (diff(lons))/2
# Reorder POC dimensions to lon, lat, depth, month
poc_l <- aperm(poc_l, c(3, 2, 1, 4))
poc_s <- aperm(poc_s, c(3, 2, 1, 4))
# Compute annual mean at all depth
poc_l <- apply(poc_l, c(1, 2, 3), mean, na.rm = TRUE)
poc_s <- apply(poc_s, c(1, 2, 3), mean, na.rm = TRUE)
## Convert to dataframe
# store matrices into a single object
poc <- c()
poc$poc_l <- poc_l
poc$poc_s <- poc_s
# unroll each matrix
poc_v <- lapply(poc, function(e) { as.vector(e) })
# combine as columns
df_argo <- do.call(cbind, poc_v) %>% as.data.frame() %>% setNames(names(poc_v))
# add coordinates (NB: shorter elements are recycled automatically)
df_argo$lon <- lons
df_argo$lat <- rep(lats, each = length(lons))
df_argo$depth <- rep(depths, each = length(lons) * length(lats))
# Reorder and compute total poc
df_argo <- df_argo %>%
mutate(poc_tot = poc_s + poc_l) %>%
select(lon, lat, depth, everything()) %>%
as_tibble()Explore data, surface data.
df_argo_sl <- df_argo %>% filter(depth == 5)
ggmap(df_argo_sl, var = "poc_tot", type = "raster") + scale_fill_cmocean(name = "matter", na.value = NA)Explore data at the base of euphotic layer, using depth bin 75.0.
See how these values compare to outputs from Wang. First, we need to round coordinates from Wang data to match output data that uses bins of:
8° in longitude
4° in latitude
t_depth <- 75 # target depth
df_argo_sl <- df_argo %>% filter(depth == t_depth)
ggmap(df_argo_sl, var = "poc_tot", type = "raster") + scale_fill_cmocean(name = "matter", na.value = NA)df_join <- df_mod %>%
mutate(
lon = roundp(lon, precision = 8, f = round),
lat = roundp(lat, precision = 4, f = round),
) %>%
group_by(lon, lat) %>%
summarise(poc_exp = mean(poc_exp), .groups = "drop") %>%
left_join(df_argo_sl %>% filter(depth == t_depth), by = join_by(lon, lat)) %>%
select(-depth) %>%
rename(poc_mod = poc_exp) %>%
pivot_longer(poc_l:poc_tot, names_to = "size_range", values_to = "poc_obs")
ggplot(df_join) +
geom_abline(slope = 1, intercept = 0, colour = "red") +
geom_point(aes(x = poc_obs, y = poc_mod, colour = abs(lat)), size = 0.5) +
scale_colour_viridis_c() +
facet_wrap(~size_range, scales = "free", ncol = 3)## Compute correlations per group
df_join %>%
select(size_range, poc_mod, poc_obs) %>%
nest(data = c(poc_mod, poc_obs)) %>%
mutate(
corr = map(data, ~cor(.$poc_mod, .$poc_obs, use = "pairwise.complete.obs"))
) %>%
unnest(corr)# A tibble: 3 × 3
size_range data corr
<chr> <list> <dbl>
1 poc_l <tibble [2,025 × 2]> -0.0472
2 poc_s <tibble [2,025 × 2]> 0.342
3 poc_tot <tibble [2,025 × 2]> 0.00628
Explore data, using depth bin 937.5, the closest to 1000 m.
t_depth <- 937.5 # target depth
df_argo_sl <- df_argo %>% filter(depth == t_depth)
ggmap(df_argo_sl, var = "poc_tot", type = "raster") + scale_fill_cmocean(name = "matter", na.value = NA)df_join <- df_mod %>%
mutate(
lon = roundp(lon, precision = 8, f = round),
lat = roundp(lat, precision = 4, f = round),
) %>%
group_by(lon, lat) %>%
summarise(poc_exp = mean(poc_exp), .groups = "drop") %>%
left_join(df_argo_sl %>% filter(depth == t_depth), by = join_by(lon, lat)) %>%
select(-depth) %>%
rename(poc_mod = poc_exp) %>%
pivot_longer(poc_l:poc_tot, names_to = "size_range", values_to = "poc_obs")
ggplot(df_join) +
geom_abline(slope = 1, intercept = 0, colour = "red") +
geom_point(aes(x = poc_obs, y = poc_mod, colour = abs(lat)), size = 0.5) +
scale_colour_viridis_c() +
facet_wrap(~size_range, scales = "free", ncol = 3)## Compute correlations per group
df_join %>%
select(size_range, poc_mod, poc_obs) %>%
nest(data = c(poc_mod, poc_obs)) %>%
mutate(
corr = map(data, ~cor(.$poc_mod, .$poc_obs, use = "pairwise.complete.obs"))
) %>%
unnest(corr)# A tibble: 3 × 3
size_range data corr
<chr> <list> <dbl>
1 poc_l <tibble [2,025 × 2]> 0.185
2 poc_s <tibble [2,025 × 2]> 0.131
3 poc_tot <tibble [2,025 × 2]> 0.198
POC attenuation
Between ~100 and ~1000 m.
Wang
df_mod <- df_mod %>% mutate(att = poc_1000 / poc_100)
ggmap(df_mod, var = "att", type = "raster") + labs(fill = "POC\natten.")ARGO
df_argo_att <- df_argo %>%
select(lon, lat, depth, poc_tot) %>%
filter(depth == 175 | depth == 475) %>%
mutate(layer = ifelse(depth == 175, "shallow", "deep")) %>%
select(-depth) %>%
pivot_wider(names_from = "layer", values_from = "poc_tot") %>%
mutate(att = deep / shallow)
ggmap(df_argo_att, var = "att", type = "raster")# Attenuation should be < 1
ggmap(df_argo_att %>% filter(att <= 1), var = "att", type = "raster")b-value for Fz = Fz0(z/z0)b
# Reference depth 75 m
z0 <- 75
# Unique identifier for pixels
df_argo <- df_argo %>%
select(lon, lat, depth, poc_tot) %>%
mutate(pix_id = paste0("lon_", lon, "_lat_", lat), .before = lon) %>%
filter(depth >= z0)
# Drop pixels with no data
df_argo <- df_argo %>%
group_by(pix_id, lon, lat) %>%
mutate(n_obs = sum(!is.na(poc_tot))) %>%
ungroup() %>%
filter(n_obs > 0)
# Select a few pixels for plots
set.seed(1)
pix_plot <- df_argo %>% select(pix_id) %>% unique() %>% slice_sample(n = 16) %>% pull(pix_id)
df_argo %>%
filter(pix_id %in% pix_plot) %>%
#filter(lon == -128) %>%
ggplot() +
geom_point(aes(x = poc_tot, y = -depth)) +
facet_wrap(~pix_id)toto <- df_argo %>%
filter(pix_id %in% pix_plot) %>%
filter(lon == -128) %>%
group_by(pix_id, lon, lat) %>%
arrange(depth) %>%
ungroup()
ggplot(toto) +
geom_point(aes(x = poc_tot, y = -depth)) +
facet_wrap(~pix_id)ggplot(toto) +
geom_point(aes(x = log(depth/z0), y = log(poc_tot))) +
facet_wrap(~pix_id)# Linear model
att_fit <- lm(log(poc_tot) ~ log(depth/z0), data = toto)
summary(att_fit)
Call:
lm(formula = log(poc_tot) ~ log(depth/z0), data = toto)
Residuals:
Min 1Q Median 3Q Max
-0.34606 -0.17043 -0.03345 0.11832 0.73952
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.90355 0.13785 35.57 < 2e-16 ***
log(depth/z0) -1.00436 0.06357 -15.80 1.72e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2526 on 22 degrees of freedom
Multiple R-squared: 0.919, Adjusted R-squared: 0.9153
F-statistic: 249.6 on 1 and 22 DF, p-value: 1.72e-13
tidy(att_fit)# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.90 0.138 35.6 6.15e-21
2 log(depth/z0) -1.00 0.0636 -15.8 1.72e-13